Portfolio forecasting tools: what you need to know

RMA Journal, The, Oct, 2003 by Joseph L. Breeden

After discussing the uses and benefits of forecasting, this article introduces the reader to three areas of analysis: historical (if you don't know where you've been, you can't know where you're going); baseline (if you don't like where you're going, change direction), and multiple-scenario (if you can't change direction, install airbags).

The world of portfolio forecasting is never static. The demands placed on forecasting continue to grow along with the challenges. At the same time, better internal data availability, new external data sources, and new modeling techniques offer hope of meeting these demands.

What Is Forecasting?

Almost every activity in lending involves some form of forecasting. For the present discussion, we focus primarily on retail loan portfolio forecasts. This is a pooled analysis of some performance metric of interest, such as attrition or prepayment, balances or utilization, delinquency, losses tees, up-sell or cross-sell, recoveries, etc. The goal is to predict monthly performance from one month to several years into the future. In this definition of forecasting we are including the annual budgeting process, delinquency and loss forecasts by credit risk, revenue forecasts by marketing, profitability forecasts by finance collections inventory forecasts, and lifetime value forecasts, just to name a few.

Any forecast is relative to a scenario for marketing and sales plans, management policies, and the macroeconomic environment, whether implicit or explicit. To make the best business decisions, we want to be able to explain how these assumptions drive the forecasts. Forecasts should therefore be scenario based--they are more useful.

Excluded from this discussion are scoring models. Scoring models usually focus on ranking account-level behavior over a period of time, such as delinquency in the next two years. Scoring models are usually designed to provide a rank ordering of accounts rather than to predict specific performance metrics in a given macroeconomic environment. Although extremely important in account decisions, scoring models are not designed to address the portfolio forecasting questions just described and are best left to a separate discussion.

Areas of Current Industry Focus

Portfolio forecasting methodologies are under constant revision. The current high-volatility environment is steering development efforts toward the following goals.

* Predicting over long time horizons--more than six months forward.

* Modeling at the vintage level.

* Incorporating macroeconomic factors into the models.

* Incorporating management scenarios into the forecasts.

* Modeling profit over the life cycle--timing mismatches between revenue and loss.

What good is forecasting?

What is the return on investment for better forecasts? With scoring models, the answer is simple. Many companies make claims such as, "Using our score will immediately lower attrition by an average of 13%. Our clients have saved on average $3 million." If you are trying to implement a better internal forecasting process, what should the sales pitch be?

The problem is that a forecast methodology is only as good as what you do with it. Accurate scenario-based forecasting should drive better strategic decision making. Unfortunately, the classic sentiment is, "If we made a good decision, it was due to our keen business insight. If we made a bad decision, it was the fault of the forecast."

Improved strategy is perhaps the highest value an organization can obtain, yet it is the most difficult to quantify. Who would admit that they would have made a poor decision if not for the forecast?

To make the case for the value of forecasting, perhaps the best place to look is the newspaper. 2002 was a year of interesting news. During that period, you could have read the following items on consumer lenders.

   "Bear Stearns's David
   Hochstim lowered his rating to
   'peer perform' from 'outperform,'
   citing 'an inability to
   confidently forecast the extent
   of an increase in losses and
   earnings.' The stock value fell
   $1.5 billion that day."

   "... announced unexpected
   credit losses in its large finance
   subsidiary that caused a one-day
   loss in market value of
   $3.4 billion."

   "We have been on a great ride
   over" the past nine years with
   the economic expansion and a
   lot of our modeling never
   picked up on negatives in the
   credit cycle. So it has been a
   challenge to get these models to
   look at the risks associated
   with the bad times."

Clearly shareholders understand the value of greater performance visibility. On a personal level, portfolio managers know that setting and meeting realistic targets can only be to their benefit. But perhaps most important, accurate forecasting has the potential to become the core of strategic planning, budgeting, and decision making.

Knowing where you've been. When planning a journey, yon need to know where you've been before you can decide where you want to go. Managing a portfolio is no different. For example, you cannot predict whether an economic recovery will bring lower losses until you know the extent to which macroeconomic changes have affected your portfolio in the past.